Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "102" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 28 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 28 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460011 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460010 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460009 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460008 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460007 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 99.75% | 99.75% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.1465 | 0.1442 | 0.1622 | nan | nan |
| 2459998 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.861917 | 0.667688 | -0.742382 | -1.141914 | 0.433820 | 1.091473 | -1.023317 | 9.844190 | 0.6165 | 0.6368 | 0.3752 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.084768 | 0.696955 | -0.524859 | -1.102068 | -0.064143 | 0.815170 | -1.029124 | 11.197075 | 0.6277 | 0.6519 | 0.3765 | nan | nan |
| 2459996 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.609261 | 1.244971 | -1.376311 | -0.460281 | 0.385138 | 0.897315 | -0.362583 | 7.245024 | 0.6377 | 0.6564 | 0.3819 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 99.51% | 99.57% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | 0.5984 | 0.6296 | 0.4760 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 99.95% | 99.95% | 0.00% | - | - | 191.102119 | 191.816394 | inf | inf | 2160.813404 | 2143.288926 | 4934.384369 | 4935.735050 | 0.4970 | 0.4379 | 0.3564 | nan | nan |
| 2459993 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.748619 | 1.358464 | -1.259279 | -0.708055 | 0.046682 | 1.081470 | 0.334780 | 6.992824 | 0.6356 | 0.6465 | 0.3745 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.819150 | 0.982308 | -1.292488 | -0.761438 | 0.435428 | 1.072928 | -0.168899 | 8.437545 | 0.6319 | 0.6456 | 0.3740 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.922971 | 1.071894 | -0.957500 | -0.431232 | 0.257394 | 1.157116 | -0.475846 | 6.893317 | 0.6261 | 0.6438 | 0.3775 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.974908 | 1.155655 | -1.393434 | -0.942346 | 0.482469 | 1.124699 | -0.127157 | 5.999508 | 0.6246 | 0.6417 | 0.3715 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.995751 | 0.779330 | -0.704945 | -1.270371 | -0.133132 | 0.387027 | -0.912991 | 4.668296 | 0.6368 | 0.6530 | 0.3698 | nan | nan |
| 2459986 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.814738 | 1.341977 | -1.389802 | -0.940443 | 0.569424 | 0.801653 | -1.026240 | 1.497459 | 0.6549 | 0.6743 | 0.3247 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.557463 | 1.328938 | -1.355923 | -0.791730 | 0.217039 | 0.500927 | -0.590449 | 6.261858 | 0.6331 | 0.6483 | 0.3759 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.710936 | 1.410376 | -0.557740 | -1.255173 | 9.859419 | -0.384503 | 0.129986 | 1.043895 | 0.6453 | 0.6632 | 0.3568 | nan | nan |
| 2459983 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.631412 | 1.310029 | -1.339404 | -0.807485 | 0.631088 | 0.847198 | -0.515072 | 2.777755 | 0.6649 | 0.6845 | 0.3137 | nan | nan |
| 2459982 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.730882 | 0.052983 | -1.078394 | -0.418638 | 0.805512 | 0.836634 | -0.816861 | 0.026855 | 0.7192 | 0.7198 | 0.2727 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.502504 | 1.293094 | -1.430219 | -0.986832 | 0.255524 | 1.118023 | -0.541622 | 7.150442 | 0.6400 | 0.6506 | 0.3714 | nan | nan |
| 2459980 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.578005 | 0.615834 | -0.950909 | -1.394433 | 0.456053 | 0.605494 | -1.281487 | -0.423086 | 0.6850 | 0.6947 | 0.2968 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.848411 | 1.102938 | -1.430616 | -0.775865 | 0.477301 | 0.871071 | 0.011150 | 8.454932 | 0.6320 | 0.6466 | 0.3728 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.885889 | 1.405218 | -1.420904 | -0.894480 | 0.550555 | 1.085641 | 0.334592 | 9.907899 | 0.6324 | 0.6452 | 0.3783 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.793359 | 0.967404 | -0.849171 | -1.399607 | 0.139199 | 0.829744 | -0.420722 | 5.187939 | 0.5994 | 0.6131 | 0.3435 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.522312 | 1.224230 | -1.534731 | -0.876681 | 0.658192 | 0.893515 | -0.777690 | 7.205540 | 0.6390 | 0.6513 | 0.3714 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 9.844190 | -0.861917 | 0.667688 | -0.742382 | -1.141914 | 0.433820 | 1.091473 | -1.023317 | 9.844190 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 11.197075 | -1.084768 | 0.696955 | -0.524859 | -1.102068 | -0.064143 | 0.815170 | -1.029124 | 11.197075 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 7.245024 | -0.609261 | 1.244971 | -1.376311 | -0.460281 | 0.385138 | 0.897315 | -0.362583 | 7.245024 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | ee Power | inf | 191.102119 | 191.816394 | inf | inf | 2160.813404 | 2143.288926 | 4934.384369 | 4935.735050 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 6.992824 | -0.748619 | 1.358464 | -1.259279 | -0.708055 | 0.046682 | 1.081470 | 0.334780 | 6.992824 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 8.437545 | 0.982308 | -0.819150 | -0.761438 | -1.292488 | 1.072928 | 0.435428 | 8.437545 | -0.168899 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 6.893317 | 1.071894 | -0.922971 | -0.431232 | -0.957500 | 1.157116 | 0.257394 | 6.893317 | -0.475846 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 5.999508 | 1.155655 | -0.974908 | -0.942346 | -1.393434 | 1.124699 | 0.482469 | 5.999508 | -0.127157 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 4.668296 | -0.995751 | 0.779330 | -0.704945 | -1.270371 | -0.133132 | 0.387027 | -0.912991 | 4.668296 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 1.497459 | 1.341977 | -0.814738 | -0.940443 | -1.389802 | 0.801653 | 0.569424 | 1.497459 | -1.026240 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 6.261858 | 1.328938 | -0.557463 | -0.791730 | -1.355923 | 0.500927 | 0.217039 | 6.261858 | -0.590449 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | ee Temporal Variability | 9.859419 | -0.710936 | 1.410376 | -0.557740 | -1.255173 | 9.859419 | -0.384503 | 0.129986 | 1.043895 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 2.777755 | -0.631412 | 1.310029 | -1.339404 | -0.807485 | 0.631088 | 0.847198 | -0.515072 | 2.777755 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Variability | 0.836634 | -0.730882 | 0.052983 | -1.078394 | -0.418638 | 0.805512 | 0.836634 | -0.816861 | 0.026855 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 7.150442 | 1.293094 | -0.502504 | -0.986832 | -1.430219 | 1.118023 | 0.255524 | 7.150442 | -0.541622 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Shape | 0.615834 | 0.615834 | -0.578005 | -1.394433 | -0.950909 | 0.605494 | 0.456053 | -0.423086 | -1.281487 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 8.454932 | -0.848411 | 1.102938 | -1.430616 | -0.775865 | 0.477301 | 0.871071 | 0.011150 | 8.454932 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 9.907899 | 1.405218 | -0.885889 | -0.894480 | -1.420904 | 1.085641 | 0.550555 | 9.907899 | 0.334592 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 5.187939 | -0.793359 | 0.967404 | -0.849171 | -1.399607 | 0.139199 | 0.829744 | -0.420722 | 5.187939 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 102 | N08 | RF_maintenance | nn Temporal Discontinuties | 7.205540 | 1.224230 | -0.522312 | -0.876681 | -1.534731 | 0.893515 | 0.658192 | 7.205540 | -0.777690 |